Reduction of Unbalance Magnetic Force and Torque Ripple in a Special Permanent Magnet Synchronous Machine by Several Multi-Objective Meta-Heuristic Algorithms
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10259289" target="_blank" >RIV/61989100:27730/25:10259289 - isvavai.cz</a>
Výsledek na webu
<a href="https://ieeexplore.ieee.org/document/11244158" target="_blank" >https://ieeexplore.ieee.org/document/11244158</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/OJIES.2025.3632189" target="_blank" >10.1109/OJIES.2025.3632189</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Reduction of Unbalance Magnetic Force and Torque Ripple in a Special Permanent Magnet Synchronous Machine by Several Multi-Objective Meta-Heuristic Algorithms
Popis výsledku v původním jazyce
Reducing the unbalanced magnetic forces (UMFs) and torque ripple (TR) simultaneously is one of the most vital goals of electrical machines designed for a variety of applications, such as hybrid vehicles. Removing TR is causing vibration free performance as well as UMFs decreasing will increase the life of machine components. Although several quantities impact the mentioned indicators, the first one is type of magnetization. Hence, two conventional magnetization patterns including radial and 9-segment magnetization patterns are thought through. Furthermore, pole arc to pole pitch ratio is extremely influential. Therefore, two functions based on it are determined for UMF and TR. Several magnetization patterns are considered to provide suitable response. They are optimized by multiobjective meta-heuristic optimization algorithms. Three algorithms including Pareto envelope-based selection algorithm II, nondominate sorting genetic algorithm II, and multiobjective particle swarm optimization have been utilized because their performances depend on not only initial guess but also type of problem. Then, the optimization results have been compared. Next, the best machine's dimensions are selected. After that, cogging, reluctance and instantaneous torque, overload capability curve and torque-speed characteristic have been computed. Finally, the temperature impact on either UMF's and torque's average or mentioned indicators is analyzed. It should be noted that the function fitting and optimization process have been done using MATLAB software.
Název v anglickém jazyce
Reduction of Unbalance Magnetic Force and Torque Ripple in a Special Permanent Magnet Synchronous Machine by Several Multi-Objective Meta-Heuristic Algorithms
Popis výsledku anglicky
Reducing the unbalanced magnetic forces (UMFs) and torque ripple (TR) simultaneously is one of the most vital goals of electrical machines designed for a variety of applications, such as hybrid vehicles. Removing TR is causing vibration free performance as well as UMFs decreasing will increase the life of machine components. Although several quantities impact the mentioned indicators, the first one is type of magnetization. Hence, two conventional magnetization patterns including radial and 9-segment magnetization patterns are thought through. Furthermore, pole arc to pole pitch ratio is extremely influential. Therefore, two functions based on it are determined for UMF and TR. Several magnetization patterns are considered to provide suitable response. They are optimized by multiobjective meta-heuristic optimization algorithms. Three algorithms including Pareto envelope-based selection algorithm II, nondominate sorting genetic algorithm II, and multiobjective particle swarm optimization have been utilized because their performances depend on not only initial guess but also type of problem. Then, the optimization results have been compared. Next, the best machine's dimensions are selected. After that, cogging, reluctance and instantaneous torque, overload capability curve and torque-speed characteristic have been computed. Finally, the temperature impact on either UMF's and torque's average or mentioned indicators is analyzed. It should be noted that the function fitting and optimization process have been done using MATLAB software.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
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OECD FORD obor
20200 - Electrical engineering, Electronic engineering, Information engineering
Návaznosti výsledku
Projekt
—
Návaznosti
O - Projekt operacniho programu
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
IEEE Open Journal of the Industrial Electronics Society
ISSN
2644-1284
e-ISSN
2644-1284
Svazek periodika
6
Číslo periodika v rámci svazku
1-10
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
10
Strana od-do
1821-1830
Kód UT WoS článku
001631855400001
EID výsledku v databázi Scopus
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